From 64c0b9e7a1bcfc54e6ed8b57274d53c3ed9bb950 Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Fri, 25 Aug 2017 17:03:17 +0100
Subject: use refactore code
---
src/Python/ccpi/fista/FISTAReconstructor.pyc | Bin 3804 -> 0 bytes
src/Python/ccpi/fista/FISTAReconstructor.py~ | 349 ------------
src/Python/ccpi/fista/Reconstructor.py~ | 598 ---------------------
src/Python/ccpi/fista/__init__.pyc | Bin 189 -> 0 bytes
.../__pycache__/FISTAReconstructor.cpython-35.pyc | Bin 3641 -> 0 bytes
.../ccpi/fista/__pycache__/__init__.cpython-35.pyc | Bin 185 -> 0 bytes
src/Python/test_reconstructor.py | 2 +-
7 files changed, 1 insertion(+), 948 deletions(-)
delete mode 100644 src/Python/ccpi/fista/FISTAReconstructor.pyc
delete mode 100644 src/Python/ccpi/fista/FISTAReconstructor.py~
delete mode 100644 src/Python/ccpi/fista/Reconstructor.py~
delete mode 100644 src/Python/ccpi/fista/__init__.pyc
delete mode 100644 src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc
delete mode 100644 src/Python/ccpi/fista/__pycache__/__init__.cpython-35.pyc
diff --git a/src/Python/ccpi/fista/FISTAReconstructor.pyc b/src/Python/ccpi/fista/FISTAReconstructor.pyc
deleted file mode 100644
index ecc4d7d..0000000
Binary files a/src/Python/ccpi/fista/FISTAReconstructor.pyc and /dev/null differ
diff --git a/src/Python/ccpi/fista/FISTAReconstructor.py~ b/src/Python/ccpi/fista/FISTAReconstructor.py~
deleted file mode 100644
index 6c7024d..0000000
--- a/src/Python/ccpi/fista/FISTAReconstructor.py~
+++ /dev/null
@@ -1,349 +0,0 @@
-# -*- coding: utf-8 -*-
-###############################################################################
-#This work is part of the Core Imaging Library developed by
-#Visual Analytics and Imaging System Group of the Science Technology
-#Facilities Council, STFC
-#
-#Copyright 2017 Edoardo Pasca, Srikanth Nagella
-#Copyright 2017 Daniil Kazantsev
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#http://www.apache.org/licenses/LICENSE-2.0
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-###############################################################################
-
-
-
-import numpy
-import h5py
-#from ccpi.reconstruction.parallelbeam import alg
-
-from Regularizer import Regularizer
-from enum import Enum
-
-import astra
-
-
-
-class FISTAReconstructor():
- '''FISTA-based reconstruction algorithm using ASTRA-toolbox
-
- '''
- # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
- # ___Input___:
- # params.[] file:
- # - .proj_geom (geometry of the projector) [required]
- # - .vol_geom (geometry of the reconstructed object) [required]
- # - .sino (vectorized in 2D or 3D sinogram) [required]
- # - .iterFISTA (iterations for the main loop, default 40)
- # - .L_const (Lipschitz constant, default Power method) )
- # - .X_ideal (ideal image, if given)
- # - .weights (statisitcal weights, size of the sinogram)
- # - .ROI (Region-of-interest, only if X_ideal is given)
- # - .initialize (a 'warm start' using SIRT method from ASTRA)
- #----------------Regularization choices------------------------
- # - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
- # - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
- # - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
- # - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
- # - .Regul_Iterations (iterations for the selected penalty, default 25)
- # - .Regul_tauLLT (time step parameter for LLT term)
- # - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
- # - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
- #----------------Visualization parameters------------------------
- # - .show (visualize reconstruction 1/0, (0 default))
- # - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
- # - .slice (for 3D volumes - slice number to imshow)
- # ___Output___:
- # 1. X - reconstructed image/volume
- # 2. output - a structure with
- # - .Resid_error - residual error (if X_ideal is given)
- # - .objective: value of the objective function
- # - .L_const: Lipshitz constant to avoid recalculations
-
- # References:
- # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
- # Problems" by A. Beck and M Teboulle
- # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
- # 3. "A novel tomographic reconstruction method based on the robust
- # Student's t function for suppressing data outliers" D. Kazantsev et.al.
- # D. Kazantsev, 2016-17
- def __init__(self, projector_geometry, output_geometry, input_sinogram, **kwargs):
- self.params = dict()
- self.params['projector_geometry'] = projector_geometry
- self.params['output_geometry'] = output_geometry
- self.params['input_sinogram'] = input_sinogram
- detectors, nangles, sliceZ = numpy.shape(input_sinogram)
- self.params['detectors'] = detectors
- self.params['number_og_angles'] = nangles
- self.params['SlicesZ'] = sliceZ
-
- # Accepted input keywords
- kw = ('number_of_iterations', 'Lipschitz_constant' , 'ideal_image' ,
- 'weights' , 'region_of_interest' , 'initialize' ,
- 'regularizer' ,
- 'ring_lambda_R_L1',
- 'ring_alpha')
-
- # handle keyworded parameters
- if kwargs is not None:
- for key, value in kwargs.items():
- if key in kw:
- #print("{0} = {1}".format(key, value))
- self.pars[key] = value
-
- # set the default values for the parameters if not set
- if 'number_of_iterations' in kwargs.keys():
- self.pars['number_of_iterations'] = kwargs['number_of_iterations']
- else:
- self.pars['number_of_iterations'] = 40
- if 'weights' in kwargs.keys():
- self.pars['weights'] = kwargs['weights']
- else:
- self.pars['weights'] = numpy.ones(numpy.shape(self.params['input_sinogram']))
- if 'Lipschitz_constant' in kwargs.keys():
- self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
- else:
- self.pars['Lipschitz_constant'] = self.calculateLipschitzConstantWithPowerMethod()
-
- if not self.pars['ideal_image'] in kwargs.keys():
- self.pars['ideal_image'] = None
-
- if not self.pars['region_of_interest'] :
- if self.pars['ideal_image'] == None:
- pass
- else:
- self.pars['region_of_interest'] = numpy.nonzero(self.pars['ideal_image']>0.0)
-
- if not self.pars['regularizer'] :
- self.pars['regularizer'] = None
- else:
- # the regularizer must be a correctly instantiated object
- if not self.pars['ring_lambda_R_L1']:
- self.pars['ring_lambda_R_L1'] = 0
- if not self.pars['ring_alpha']:
- self.pars['ring_alpha'] = 1
-
-
-
-
- def calculateLipschitzConstantWithPowerMethod(self):
- ''' using Power method (PM) to establish L constant'''
-
- #N = params.vol_geom.GridColCount
- N = self.pars['output_geometry'].GridColCount
- proj_geom = self.params['projector_geometry']
- vol_geom = self.params['output_geometry']
- weights = self.pars['weights']
- SlicesZ = self.pars['SlicesZ']
-
- if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
- #% for parallel geometry we can do just one slice
- #fprintf('%s \n', 'Calculating Lipshitz constant for parallel beam geometry...');
- niter = 15;# % number of iteration for the PM
- #N = params.vol_geom.GridColCount;
- #x1 = rand(N,N,1);
- x1 = numpy.random.rand(1,N,N)
- #sqweight = sqrt(weights(:,:,1));
- sqweight = numpy.sqrt(weights.T[0])
- proj_geomT = proj_geom.copy();
- proj_geomT.DetectorRowCount = 1;
- vol_geomT = vol_geom.copy();
- vol_geomT['GridSliceCount'] = 1;
-
-
- for i in range(niter):
- if i == 0:
- #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
- sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
- y = sqweight * y # element wise multiplication
- #astra_mex_data3d('delete', sino_id);
- astra.matlab.data3d('delete', sino_id)
-
- idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, proj_geomT, vol_geomT);
- s = numpy.linalg.norm(x1)
- ### this line?
- x1 = x1/s;
- ### this line?
- sino_id, y = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
- y = sqweight*y;
- astra.matlab.data3d('delete', sino_id);
- astra.matlab.data3d('delete', idx);
- #end
- del proj_geomT
- del vol_geomT
- else
- #% divergen beam geometry
- #fprintf('%s \n', 'Calculating Lipshitz constant for divergen beam geometry...');
- niter = 8; #% number of iteration for PM
- x1 = numpy.random.rand(SlicesZ , N , N);
- #sqweight = sqrt(weights);
- sqweight = numpy.sqrt(weights.T[0])
-
- sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
- y = sqweight*y;
- #astra_mex_data3d('delete', sino_id);
- astra.matlab.data3d('delete', sino_id);
-
- for i in range(niter):
- #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
- idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y,
- proj_geom,
- vol_geom)
- s = numpy.linalg.norm(x1)
- ### this line?
- x1 = x1/s;
- ### this line?
- #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
- sino_id, y = astra.creators.create_sino3d_gpu(x1,
- proj_geom,
- vol_geom);
-
- y = sqweight*y;
- #astra_mex_data3d('delete', sino_id);
- #astra_mex_data3d('delete', id);
- astra.matlab.data3d('delete', sino_id);
- astra.matlab.data3d('delete', idx);
- #end
- #clear x1
- del x1
-
- return s
-
-
- def setRegularizer(self, regularizer):
- if regularizer
- self.pars['regularizer'] = regularizer
-
-
-
-
-
-def getEntry(location):
- for item in nx[location].keys():
- print (item)
-
-
-print ("Loading Data")
-
-##fname = "D:\\Documents\\Dataset\\IMAT\\20170419_crabtomo\\crabtomo\\Sample\\IMAT00005153_crabstomo_Sample_000.tif"
-####ind = [i * 1049 for i in range(360)]
-#### use only 360 images
-##images = 200
-##ind = [int(i * 1049 / images) for i in range(images)]
-##stack_image = dxchange.reader.read_tiff_stack(fname, ind, digit=None, slc=None)
-
-#fname = "D:\\Documents\\Dataset\\CGLS\\24737_fd.nxs"
-fname = "C:\\Users\\ofn77899\\Documents\\CCPi\\CGLS\\24737_fd_2.nxs"
-nx = h5py.File(fname, "r")
-
-# the data are stored in a particular location in the hdf5
-for item in nx['entry1/tomo_entry/data'].keys():
- print (item)
-
-data = nx.get('entry1/tomo_entry/data/rotation_angle')
-angles = numpy.zeros(data.shape)
-data.read_direct(angles)
-print (angles)
-# angles should be in degrees
-
-data = nx.get('entry1/tomo_entry/data/data')
-stack = numpy.zeros(data.shape)
-data.read_direct(stack)
-print (data.shape)
-
-print ("Data Loaded")
-
-
-# Normalize
-data = nx.get('entry1/tomo_entry/instrument/detector/image_key')
-itype = numpy.zeros(data.shape)
-data.read_direct(itype)
-# 2 is dark field
-darks = [stack[i] for i in range(len(itype)) if itype[i] == 2 ]
-dark = darks[0]
-for i in range(1, len(darks)):
- dark += darks[i]
-dark = dark / len(darks)
-#dark[0][0] = dark[0][1]
-
-# 1 is flat field
-flats = [stack[i] for i in range(len(itype)) if itype[i] == 1 ]
-flat = flats[0]
-for i in range(1, len(flats)):
- flat += flats[i]
-flat = flat / len(flats)
-#flat[0][0] = dark[0][1]
-
-
-# 0 is projection data
-proj = [stack[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = [angles[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = numpy.asarray (angle_proj)
-angle_proj = angle_proj.astype(numpy.float32)
-
-# normalized data are
-# norm = (projection - dark)/(flat-dark)
-
-def normalize(projection, dark, flat, def_val=0.1):
- a = (projection - dark)
- b = (flat-dark)
- with numpy.errstate(divide='ignore', invalid='ignore'):
- c = numpy.true_divide( a, b )
- c[ ~ numpy.isfinite( c )] = def_val # set to not zero if 0/0
- return c
-
-
-norm = [normalize(projection, dark, flat) for projection in proj]
-norm = numpy.asarray (norm)
-norm = norm.astype(numpy.float32)
-
-
-niterations = 15
-threads = 3
-
-img_cgls = alg.cgls(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_mlem = alg.mlem(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_sirt = alg.sirt(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_conv = alg.cgls_conv(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
- iteration_values, False)
-print ("iteration values %s" % str(iteration_values))
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_tikhonov = alg.cgls_tikhonov(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
- numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-iteration_values = numpy.zeros((niterations,))
-img_cgls_TVreg = alg.cgls_TVreg(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
- numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-
-
-##numpy.save("cgls_recon.npy", img_data)
-import matplotlib.pyplot as plt
-fig, ax = plt.subplots(1,6,sharey=True)
-ax[0].imshow(img_cgls[80])
-ax[0].axis('off') # clear x- and y-axes
-ax[1].imshow(img_sirt[80])
-ax[1].axis('off') # clear x- and y-axes
-ax[2].imshow(img_mlem[80])
-ax[2].axis('off') # clear x- and y-axesplt.show()
-ax[3].imshow(img_cgls_conv[80])
-ax[3].axis('off') # clear x- and y-axesplt.show()
-ax[4].imshow(img_cgls_tikhonov[80])
-ax[4].axis('off') # clear x- and y-axesplt.show()
-ax[5].imshow(img_cgls_TVreg[80])
-ax[5].axis('off') # clear x- and y-axesplt.show()
-
-
-plt.show()
-
-
diff --git a/src/Python/ccpi/fista/Reconstructor.py~ b/src/Python/ccpi/fista/Reconstructor.py~
deleted file mode 100644
index ba67327..0000000
--- a/src/Python/ccpi/fista/Reconstructor.py~
+++ /dev/null
@@ -1,598 +0,0 @@
-# -*- coding: utf-8 -*-
-###############################################################################
-#This work is part of the Core Imaging Library developed by
-#Visual Analytics and Imaging System Group of the Science Technology
-#Facilities Council, STFC
-#
-#Copyright 2017 Edoardo Pasca, Srikanth Nagella
-#Copyright 2017 Daniil Kazantsev
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#http://www.apache.org/licenses/LICENSE-2.0
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-###############################################################################
-
-
-
-import numpy
-import h5py
-from ccpi.reconstruction.parallelbeam import alg
-
-from Regularizer import Regularizer
-from enum import Enum
-
-import astra
-
-
-class Reconstructor:
-
- class Algorithm(Enum):
- CGLS = alg.cgls
- CGLS_CONV = alg.cgls_conv
- SIRT = alg.sirt
- MLEM = alg.mlem
- CGLS_TICHONOV = alg.cgls_tikhonov
- CGLS_TVREG = alg.cgls_TVreg
- FISTA = 'fista'
-
- def __init__(self, algorithm = None, projection_data = None,
- angles = None, center_of_rotation = None ,
- flat_field = None, dark_field = None,
- iterations = None, resolution = None, isLogScale = False, threads = None,
- normalized_projection = None):
-
- self.pars = dict()
- self.pars['algorithm'] = algorithm
- self.pars['projection_data'] = projection_data
- self.pars['normalized_projection'] = normalized_projection
- self.pars['angles'] = angles
- self.pars['center_of_rotation'] = numpy.double(center_of_rotation)
- self.pars['flat_field'] = flat_field
- self.pars['iterations'] = iterations
- self.pars['dark_field'] = dark_field
- self.pars['resolution'] = resolution
- self.pars['isLogScale'] = isLogScale
- self.pars['threads'] = threads
- if (iterations != None):
- self.pars['iterationValues'] = numpy.zeros((iterations))
-
- if projection_data != None and dark_field != None and flat_field != None:
- norm = self.normalize(projection_data, dark_field, flat_field, 0.1)
- self.pars['normalized_projection'] = norm
-
-
- def setPars(self, parameters):
- keys = ['algorithm','projection_data' ,'normalized_projection', \
- 'angles' , 'center_of_rotation' , 'flat_field', \
- 'iterations','dark_field' , 'resolution', 'isLogScale' , \
- 'threads' , 'iterationValues', 'regularize']
-
- for k in keys:
- if k not in parameters.keys():
- self.pars[k] = None
- else:
- self.pars[k] = parameters[k]
-
-
- def sanityCheck(self):
- projection_data = self.pars['projection_data']
- dark_field = self.pars['dark_field']
- flat_field = self.pars['flat_field']
- angles = self.pars['angles']
-
- if projection_data != None and dark_field != None and \
- angles != None and flat_field != None:
- data_shape = numpy.shape(projection_data)
- angle_shape = numpy.shape(angles)
-
- if angle_shape[0] != data_shape[0]:
- #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \
- # (angle_shape[0] , data_shape[0]) )
- return (False , 'Projections and angles dimensions do not match: %d vs %d' % \
- (angle_shape[0] , data_shape[0]) )
-
- if data_shape[1:] != numpy.shape(flat_field):
- #raise Exception('Projection and flat field dimensions do not match')
- return (False , 'Projection and flat field dimensions do not match')
- if data_shape[1:] != numpy.shape(dark_field):
- #raise Exception('Projection and dark field dimensions do not match')
- return (False , 'Projection and dark field dimensions do not match')
-
- return (True , '' )
- elif self.pars['normalized_projection'] != None:
- data_shape = numpy.shape(self.pars['normalized_projection'])
- angle_shape = numpy.shape(angles)
-
- if angle_shape[0] != data_shape[0]:
- #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \
- # (angle_shape[0] , data_shape[0]) )
- return (False , 'Projections and angles dimensions do not match: %d vs %d' % \
- (angle_shape[0] , data_shape[0]) )
- else:
- return (True , '' )
- else:
- return (False , 'Not enough data')
-
- def reconstruct(self, parameters = None):
- if parameters != None:
- self.setPars(parameters)
-
- go , reason = self.sanityCheck()
- if go:
- return self._reconstruct()
- else:
- raise Exception(reason)
-
-
- def _reconstruct(self, parameters=None):
- if parameters!=None:
- self.setPars(parameters)
- parameters = self.pars
-
- if parameters['algorithm'] != None and \
- parameters['normalized_projection'] != None and \
- parameters['angles'] != None and \
- parameters['center_of_rotation'] != None and \
- parameters['iterations'] != None and \
- parameters['resolution'] != None and\
- parameters['threads'] != None and\
- parameters['isLogScale'] != None:
-
-
- if parameters['algorithm'] in (Reconstructor.Algorithm.CGLS,
- Reconstructor.Algorithm.MLEM, Reconstructor.Algorithm.SIRT):
- #store parameters
- self.pars = parameters
- result = parameters['algorithm'](
- parameters['normalized_projection'] ,
- parameters['angles'],
- parameters['center_of_rotation'],
- parameters['resolution'],
- parameters['iterations'],
- parameters['threads'] ,
- parameters['isLogScale']
- )
- return result
- elif parameters['algorithm'] in (Reconstructor.Algorithm.CGLS_CONV,
- Reconstructor.Algorithm.CGLS_TICHONOV,
- Reconstructor.Algorithm.CGLS_TVREG) :
- self.pars = parameters
- result = parameters['algorithm'](
- parameters['normalized_projection'] ,
- parameters['angles'],
- parameters['center_of_rotation'],
- parameters['resolution'],
- parameters['iterations'],
- parameters['threads'] ,
- parameters['regularize'],
- numpy.zeros((parameters['iterations'])),
- parameters['isLogScale']
- )
-
- elif parameters['algorithm'] == Reconstructor.Algorithm.FISTA:
- pass
-
- else:
- if parameters['projection_data'] != None and \
- parameters['dark_field'] != None and \
- parameters['flat_field'] != None:
- norm = self.normalize(parameters['projection_data'],
- parameters['dark_field'],
- parameters['flat_field'], 0.1)
- self.pars['normalized_projection'] = norm
- return self._reconstruct(parameters)
-
-
-
- def _normalize(self, projection, dark, flat, def_val=0):
- a = (projection - dark)
- b = (flat-dark)
- with numpy.errstate(divide='ignore', invalid='ignore'):
- c = numpy.true_divide( a, b )
- c[ ~ numpy.isfinite( c )] = def_val # set to not zero if 0/0
- return c
-
- def normalize(self, projections, dark, flat, def_val=0):
- norm = [self._normalize(projection, dark, flat, def_val) for projection in projections]
- return numpy.asarray (norm, dtype=numpy.float32)
-
-
-
-class FISTA():
- '''FISTA-based reconstruction algorithm using ASTRA-toolbox
-
- '''
- # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
- # ___Input___:
- # params.[] file:
- # - .proj_geom (geometry of the projector) [required]
- # - .vol_geom (geometry of the reconstructed object) [required]
- # - .sino (vectorized in 2D or 3D sinogram) [required]
- # - .iterFISTA (iterations for the main loop, default 40)
- # - .L_const (Lipschitz constant, default Power method) )
- # - .X_ideal (ideal image, if given)
- # - .weights (statisitcal weights, size of the sinogram)
- # - .ROI (Region-of-interest, only if X_ideal is given)
- # - .initialize (a 'warm start' using SIRT method from ASTRA)
- #----------------Regularization choices------------------------
- # - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
- # - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
- # - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
- # - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
- # - .Regul_Iterations (iterations for the selected penalty, default 25)
- # - .Regul_tauLLT (time step parameter for LLT term)
- # - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
- # - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
- #----------------Visualization parameters------------------------
- # - .show (visualize reconstruction 1/0, (0 default))
- # - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
- # - .slice (for 3D volumes - slice number to imshow)
- # ___Output___:
- # 1. X - reconstructed image/volume
- # 2. output - a structure with
- # - .Resid_error - residual error (if X_ideal is given)
- # - .objective: value of the objective function
- # - .L_const: Lipshitz constant to avoid recalculations
-
- # References:
- # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
- # Problems" by A. Beck and M Teboulle
- # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
- # 3. "A novel tomographic reconstruction method based on the robust
- # Student's t function for suppressing data outliers" D. Kazantsev et.al.
- # D. Kazantsev, 2016-17
- def __init__(self, projector_geometry, output_geometry, input_sinogram, **kwargs):
- self.params = dict()
- self.params['projector_geometry'] = projector_geometry
- self.params['output_geometry'] = output_geometry
- self.params['input_sinogram'] = input_sinogram
- detectors, nangles, sliceZ = numpy.shape(input_sinogram)
- self.params['detectors'] = detectors
- self.params['number_og_angles'] = nangles
- self.params['SlicesZ'] = sliceZ
-
- # Accepted input keywords
- kw = ('number_of_iterations', 'Lipschitz_constant' , 'ideal_image' ,
- 'weights' , 'region_of_interest' , 'initialize' ,
- 'regularizer' ,
- 'ring_lambda_R_L1',
- 'ring_alpha')
-
- # handle keyworded parameters
- if kwargs is not None:
- for key, value in kwargs.items():
- if key in kw:
- #print("{0} = {1}".format(key, value))
- self.pars[key] = value
-
- # set the default values for the parameters if not set
- if 'number_of_iterations' in kwargs.keys():
- self.pars['number_of_iterations'] = kwargs['number_of_iterations']
- else:
- self.pars['number_of_iterations'] = 40
- if 'weights' in kwargs.keys():
- self.pars['weights'] = kwargs['weights']
- else:
- self.pars['weights'] = numpy.ones(numpy.shape(self.params['input_sinogram']))
- if 'Lipschitz_constant' in kwargs.keys():
- self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
- else:
- self.pars['Lipschitz_constant'] = self.calculateLipschitzConstantWithPowerMethod()
-
- if not self.pars['ideal_image'] in kwargs.keys():
- self.pars['ideal_image'] = None
-
- if not self.pars['region_of_interest'] :
- if self.pars['ideal_image'] == None:
- pass
- else:
- self.pars['region_of_interest'] = numpy.nonzero(self.pars['ideal_image']>0.0)
-
- if not self.pars['regularizer'] :
- self.pars['regularizer'] = None
- else:
- # the regularizer must be a correctly instantiated object
- if not self.pars['ring_lambda_R_L1']:
- self.pars['ring_lambda_R_L1'] = 0
- if not self.pars['ring_alpha']:
- self.pars['ring_alpha'] = 1
-
-
-
-
- def calculateLipschitzConstantWithPowerMethod(self):
- ''' using Power method (PM) to establish L constant'''
-
- #N = params.vol_geom.GridColCount
- N = self.pars['output_geometry'].GridColCount
- proj_geom = self.params['projector_geometry']
- vol_geom = self.params['output_geometry']
- weights = self.pars['weights']
- SlicesZ = self.pars['SlicesZ']
-
- if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
- #% for parallel geometry we can do just one slice
- #fprintf('%s \n', 'Calculating Lipshitz constant for parallel beam geometry...');
- niter = 15;# % number of iteration for the PM
- #N = params.vol_geom.GridColCount;
- #x1 = rand(N,N,1);
- x1 = numpy.random.rand(1,N,N)
- #sqweight = sqrt(weights(:,:,1));
- sqweight = numpy.sqrt(weights.T[0])
- proj_geomT = proj_geom.copy();
- proj_geomT.DetectorRowCount = 1;
- vol_geomT = vol_geom.copy();
- vol_geomT['GridSliceCount'] = 1;
-
-
- for i in range(niter):
- if i == 0:
- #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
- sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
- y = sqweight * y # element wise multiplication
- #astra_mex_data3d('delete', sino_id);
- astra.matlab.data3d('delete', sino_id)
-
- idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, proj_geomT, vol_geomT);
- s = numpy.linalg.norm(x1)
- ### this line?
- x1 = x1/s;
- ### this line?
- sino_id, y = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
- y = sqweight*y;
- astra.matlab.data3d('delete', sino_id);
- astra.matlab.data3d('delete', idx);
- #end
- del proj_geomT
- del vol_geomT
- else
- #% divergen beam geometry
- #fprintf('%s \n', 'Calculating Lipshitz constant for divergen beam geometry...');
- niter = 8; #% number of iteration for PM
- x1 = numpy.random.rand(SlicesZ , N , N);
- #sqweight = sqrt(weights);
- sqweight = numpy.sqrt(weights.T[0])
-
- sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
- y = sqweight*y;
- #astra_mex_data3d('delete', sino_id);
- astra.matlab.data3d('delete', sino_id);
-
- for i in range(niter):
- #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
- idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y,
- proj_geom,
- vol_geom)
- s = numpy.linalg.norm(x1)
- ### this line?
- x1 = x1/s;
- ### this line?
- #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
- sino_id, y = astra.creators.create_sino3d_gpu(x1,
- proj_geom,
- vol_geom);
-
- y = sqweight*y;
- #astra_mex_data3d('delete', sino_id);
- #astra_mex_data3d('delete', id);
- astra.matlab.data3d('delete', sino_id);
- astra.matlab.data3d('delete', idx);
- #end
- #clear x1
- del x1
-
- return s
-
-
- def setRegularizer(self, regularizer):
- if regularizer
- self.pars['regularizer'] = regularizer
-
-
-
-
-
-def getEntry(location):
- for item in nx[location].keys():
- print (item)
-
-
-print ("Loading Data")
-
-##fname = "D:\\Documents\\Dataset\\IMAT\\20170419_crabtomo\\crabtomo\\Sample\\IMAT00005153_crabstomo_Sample_000.tif"
-####ind = [i * 1049 for i in range(360)]
-#### use only 360 images
-##images = 200
-##ind = [int(i * 1049 / images) for i in range(images)]
-##stack_image = dxchange.reader.read_tiff_stack(fname, ind, digit=None, slc=None)
-
-#fname = "D:\\Documents\\Dataset\\CGLS\\24737_fd.nxs"
-fname = "C:\\Users\\ofn77899\\Documents\\CCPi\\CGLS\\24737_fd_2.nxs"
-nx = h5py.File(fname, "r")
-
-# the data are stored in a particular location in the hdf5
-for item in nx['entry1/tomo_entry/data'].keys():
- print (item)
-
-data = nx.get('entry1/tomo_entry/data/rotation_angle')
-angles = numpy.zeros(data.shape)
-data.read_direct(angles)
-print (angles)
-# angles should be in degrees
-
-data = nx.get('entry1/tomo_entry/data/data')
-stack = numpy.zeros(data.shape)
-data.read_direct(stack)
-print (data.shape)
-
-print ("Data Loaded")
-
-
-# Normalize
-data = nx.get('entry1/tomo_entry/instrument/detector/image_key')
-itype = numpy.zeros(data.shape)
-data.read_direct(itype)
-# 2 is dark field
-darks = [stack[i] for i in range(len(itype)) if itype[i] == 2 ]
-dark = darks[0]
-for i in range(1, len(darks)):
- dark += darks[i]
-dark = dark / len(darks)
-#dark[0][0] = dark[0][1]
-
-# 1 is flat field
-flats = [stack[i] for i in range(len(itype)) if itype[i] == 1 ]
-flat = flats[0]
-for i in range(1, len(flats)):
- flat += flats[i]
-flat = flat / len(flats)
-#flat[0][0] = dark[0][1]
-
-
-# 0 is projection data
-proj = [stack[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = [angles[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = numpy.asarray (angle_proj)
-angle_proj = angle_proj.astype(numpy.float32)
-
-# normalized data are
-# norm = (projection - dark)/(flat-dark)
-
-def normalize(projection, dark, flat, def_val=0.1):
- a = (projection - dark)
- b = (flat-dark)
- with numpy.errstate(divide='ignore', invalid='ignore'):
- c = numpy.true_divide( a, b )
- c[ ~ numpy.isfinite( c )] = def_val # set to not zero if 0/0
- return c
-
-
-norm = [normalize(projection, dark, flat) for projection in proj]
-norm = numpy.asarray (norm)
-norm = norm.astype(numpy.float32)
-
-#recon = Reconstructor(algorithm = Algorithm.CGLS, normalized_projection = norm,
-# angles = angle_proj, center_of_rotation = 86.2 ,
-# flat_field = flat, dark_field = dark,
-# iterations = 15, resolution = 1, isLogScale = False, threads = 3)
-
-#recon = Reconstructor(algorithm = Reconstructor.Algorithm.CGLS, projection_data = proj,
-# angles = angle_proj, center_of_rotation = 86.2 ,
-# flat_field = flat, dark_field = dark,
-# iterations = 15, resolution = 1, isLogScale = False, threads = 3)
-#img_cgls = recon.reconstruct()
-#
-#pars = dict()
-#pars['algorithm'] = Reconstructor.Algorithm.SIRT
-#pars['projection_data'] = proj
-#pars['angles'] = angle_proj
-#pars['center_of_rotation'] = numpy.double(86.2)
-#pars['flat_field'] = flat
-#pars['iterations'] = 15
-#pars['dark_field'] = dark
-#pars['resolution'] = 1
-#pars['isLogScale'] = False
-#pars['threads'] = 3
-#
-#img_sirt = recon.reconstruct(pars)
-#
-#recon.pars['algorithm'] = Reconstructor.Algorithm.MLEM
-#img_mlem = recon.reconstruct()
-
-############################################################
-############################################################
-#recon.pars['algorithm'] = Reconstructor.Algorithm.CGLS_CONV
-#recon.pars['regularize'] = numpy.double(0.1)
-#img_cgls_conv = recon.reconstruct()
-
-niterations = 15
-threads = 3
-
-img_cgls = alg.cgls(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_mlem = alg.mlem(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_sirt = alg.sirt(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_conv = alg.cgls_conv(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
- iteration_values, False)
-print ("iteration values %s" % str(iteration_values))
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_tikhonov = alg.cgls_tikhonov(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
- numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-iteration_values = numpy.zeros((niterations,))
-img_cgls_TVreg = alg.cgls_TVreg(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
- numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-
-
-##numpy.save("cgls_recon.npy", img_data)
-import matplotlib.pyplot as plt
-fig, ax = plt.subplots(1,6,sharey=True)
-ax[0].imshow(img_cgls[80])
-ax[0].axis('off') # clear x- and y-axes
-ax[1].imshow(img_sirt[80])
-ax[1].axis('off') # clear x- and y-axes
-ax[2].imshow(img_mlem[80])
-ax[2].axis('off') # clear x- and y-axesplt.show()
-ax[3].imshow(img_cgls_conv[80])
-ax[3].axis('off') # clear x- and y-axesplt.show()
-ax[4].imshow(img_cgls_tikhonov[80])
-ax[4].axis('off') # clear x- and y-axesplt.show()
-ax[5].imshow(img_cgls_TVreg[80])
-ax[5].axis('off') # clear x- and y-axesplt.show()
-
-
-plt.show()
-
-#viewer = edo.CILViewer()
-#viewer.setInputAsNumpy(img_cgls2)
-#viewer.displaySliceActor(0)
-#viewer.startRenderLoop()
-
-import vtk
-
-def NumpyToVTKImageData(numpyarray):
- if (len(numpy.shape(numpyarray)) == 3):
- doubleImg = vtk.vtkImageData()
- shape = numpy.shape(numpyarray)
- doubleImg.SetDimensions(shape[0], shape[1], shape[2])
- doubleImg.SetOrigin(0,0,0)
- doubleImg.SetSpacing(1,1,1)
- doubleImg.SetExtent(0, shape[0]-1, 0, shape[1]-1, 0, shape[2]-1)
- #self.img3D.SetScalarType(vtk.VTK_UNSIGNED_SHORT, vtk.vtkInformation())
- doubleImg.AllocateScalars(vtk.VTK_DOUBLE,1)
-
- for i in range(shape[0]):
- for j in range(shape[1]):
- for k in range(shape[2]):
- doubleImg.SetScalarComponentFromDouble(
- i,j,k,0, numpyarray[i][j][k])
- #self.setInput3DData( numpy_support.numpy_to_vtk(numpyarray) )
- # rescale to appropriate VTK_UNSIGNED_SHORT
- stats = vtk.vtkImageAccumulate()
- stats.SetInputData(doubleImg)
- stats.Update()
- iMin = stats.GetMin()[0]
- iMax = stats.GetMax()[0]
- scale = vtk.VTK_UNSIGNED_SHORT_MAX / (iMax - iMin)
-
- shiftScaler = vtk.vtkImageShiftScale ()
- shiftScaler.SetInputData(doubleImg)
- shiftScaler.SetScale(scale)
- shiftScaler.SetShift(iMin)
- shiftScaler.SetOutputScalarType(vtk.VTK_UNSIGNED_SHORT)
- shiftScaler.Update()
- return shiftScaler.GetOutput()
-
-#writer = vtk.vtkMetaImageWriter()
-#writer.SetFileName(alg + "_recon.mha")
-#writer.SetInputData(NumpyToVTKImageData(img_cgls2))
-#writer.Write()
diff --git a/src/Python/ccpi/fista/__init__.pyc b/src/Python/ccpi/fista/__init__.pyc
deleted file mode 100644
index 719e264..0000000
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diff --git a/src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc b/src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc
deleted file mode 100644
index 84f16e2..0000000
Binary files a/src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc and /dev/null differ
diff --git a/src/Python/ccpi/fista/__pycache__/__init__.cpython-35.pyc b/src/Python/ccpi/fista/__pycache__/__init__.cpython-35.pyc
deleted file mode 100644
index 90c23ff..0000000
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diff --git a/src/Python/test_reconstructor.py b/src/Python/test_reconstructor.py
index 6f46e96..a4a622b 100644
--- a/src/Python/test_reconstructor.py
+++ b/src/Python/test_reconstructor.py
@@ -9,7 +9,7 @@ Based on DemoRD2.m
import h5py
import numpy
-from ccpi.reconstruction_dev.FISTAReconstructor import FISTAReconstructor
+from ccpi.fista.FISTAReconstructor import FISTAReconstructor
import astra
##def getEntry(nx, location):
--
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